Transform an unsustainable suburb into a diverse, affordable, and walkable town. Remove single-family housing limits to let the citizens build, then connect buildings with streetcar lines of your own color to score points..
Generated at 2023-06-26, 22:45 from 1000 logged games.
Rules
Representative game (in the sense of being of mean length). Wherever you see the 'representative game' referred to in later sections, this is it!
Playing the Game
On the first turn of the game, the first player Draws Streetcar Lines only.
Thereafter, on your turn Remove 1 Housing Limit then Draw Streetcar Lines.
The game ends when no housing limits are available to remove.
Drawing Streetcar Lines
Place streetcar lines on 2 adjacent grid lines.
Your lines may branch, but different-colored lines may not touch.
Removing Housing Limits
Remove 1 house (representing a single-family housing limit). Keep it for yourself. You may remove any house, except houses next to buildings due to anti-development backlash.
If any empty space neighbors exactly 3 houses, place a building there. The neighboring houses determine the new building?s color and height:
2 houses of the same color: building that color, height 2
3 houses of the same color: building that color, height 3
3 houses of different colors: building the missing color, height 1
It is possible to place several buildings in a single turn.
Scoring
At the end of the game, each house you removed scores points equal to the combined heights of buildings of the same color connected to your lines. A building connects to all lines touching its neighboring intersections, but counts only once per player. Each separate line of your color incurs a 10 point penalty. The highest score wins. If there is a tie, the highest score in a single color wins.
This is a solid two-player abstract that I’d like to try more. We’ll see if it has staying power. This could rise in rating after more plays.
Rontuaru
7
A sleek and charming "inverted" route building abstract, where you have emergent valuable hotspots and no shortage of shared incentives. Shades of Fresh Fish, without the baroque expropriation (and so makes this immediately more approachable). The focus is more on speculation, as investing in certain colors will unlock the actual point-scoring structures on board, but the degree to which they emerge (value and location) revolves around precisely how you remove colors from the board, so there's a palpable spatial element to the game as well. The route blocking can be quite subtle, too, as you find the negative-points penalty (from starting a disconnected route) starts to be overshadowed by what you gain and what you deny your opponent. Splendid stuff.
sareneFactorial
7
clownfeet
6.5
mafman6
N/A
good 'weight of your own choices' type of game easy to learn and fast for the game's depth/challenge saw a bit of the final tweaks thanks to the designer posting in WIP and saw this crisp up from a game I was intrigued about to a game that shines. The idea of having the flat house go to the high rise works great not only thematically but as a visual aid for the players. Just a fantastic example of how the design of a component can help the game. This game also works best in its dry erase setup as intended.
BloodyMe
8
P1rD
7
dukane
8
Very cool abstract game with an amazing theme. There's a spatial element in blocking each other as you try to get to valuable spots while keeping your opponents out. Then there's the stock portion of the game and manipulating the SFH zoning in order to set yourself up to develop along your route. Very impressed with my first play!
UanarchyK
8
A lot going on for a fairly simple game. There's the push-pull of the colors, and the multiplication ramping that up. Then there's the board play and blocking, denying your opponent. And then there's the networks penalty... I kind of want to own a preposterously overproduced version of this game with 3-D printed tower blocks and everything. Rules need cleaning up though, which is funny to say about a game with a single sheet of rules. But they could be streamlined/clarified even further.
OMD56
8
Drew really has a cool design that you can tell is a passion project due to the credits for Strong Towns in the rules. This is a game for so many gamers, whether you like train games, abstracts, or euros. It has things from all of those, like gathering "shares," going after scoring opportunities, thinking about the ramifications of each play, weighing the risk and rewards, etc. After our first play, the simple comparisons we came up with were the dynamic city like Town Center and Alien City, the network and scoring chances you'd find in Fresh Fish, and the simple rules like you'd find in a great abstract or a Winsome/cube rails game. Hoping more people take a chance at printing this one up and Drew shows this to a publisher, cause he has a winner on his hands with Streetcar Suburb.
RichardIngram
9
Cubesonamap
9
Oh, boy... This is incredible. Can't wait to play again. PnP.
Kolomogorov Complexity Analysis
Size (bytes)
35847
Reference Size
10673
Ratio
3.36
Ai Ai calculates the size of the implementation, and compares it to the Ai Ai implementation of the simplest possible game (which just fills the board). Note that this estimate may include some graphics and heuristics code as well as the game logic. See the wikipedia entry for more details.
Playout Complexity Estimate
Playouts per second
12297.39 (81.32µs/playout)
Reference Size
539752.79 (1.85µs/playout)
Ratio (low is good)
43.89
Tavener complexity: the heat generated by playing every possible instance of a game with a perfectly efficient programme. Since this is not possible to calculate, Ai Ai calculates the number of random playouts per second and compares it to the fastest non-trivial Ai Ai game (Connect 4). This ratio gives a practical indication of how complex the game is. Combine this with the computational state space, and you can get an idea of how strong the default (MCTS-based) AI will be.
State Space Complexity
% new positions/bucket
State Space Complexity
471369368
State Space Complexity bounds
64146244 < 471369368 < ∞
State Space Complexity (log 10)
8.67
State Space Complexity bounds (log 10)
7.81 <= 8.67 <= ∞
Samples
817902
Confidence
0.00
0: totally unreliable, 100: perfect
State space complexity (where present) is an estimate of the number of distinct game tree reachable through actual play. Over a series of random games, Ai Ai checks each position to see if it is new, or a repeat of a previous position and keeps a total for each game. As the number of games increase, the quantity of new positions seen per game decreases. These games are then partitioned into a number of buckets, and if certain conditions are met, Ai Ai treats the number in each bucket as the start of a strictly decreasing geometric sequence and sums it to estimate the total state space. The accuracy is calculated as 1-[end bucket count]/[starting bucklet count]
Playout/Search Speed
Label
Its/s
SD
Nodes/s
SD
Game length
SD
Random playout
12,472
54
1,014,201
4,311
81
4
search.UCT
12,725
242
79
4
Random: 10 second warmup for the hotspot compiler. 100 trials of 1000ms each.
Other: 100 playouts, means calculated over the first 5 moves only to avoid distortion due to speedup at end of game.
Mirroring Strategies
Rotation (Half turn) lost each game as expected.
Reflection (X axis) lost each game as expected.
Reflection (Y axis) lost each game as expected.
Copy last move lost each game as expected.
Mirroring strategies attempt to copy the previous move. On first move, they will attempt to play in the centre. If neither of these are possible, they will pick a random move. Each entry represents a different form of copying; direct copy, reflection in either the X or Y axis, half-turn rotation.
Win % By Player (Bias)
1: Red win %
47.50±3.08
Includes draws = 50%
2: Purple win %
52.50±3.10
Includes draws = 50%
Draw %
0.00
Percentage of games where all players draw.
Decisive %
100.00
Percentage of games with a single winner.
Samples
1000
Quantity of logged games played
Note: that win/loss statistics may vary depending on thinking time (horizon effect, etc.), bad heuristics, bugs, and other factors, so should be taken with a pinch of salt. (Given perfect play, any game of pure skill will always end in the same result.)
Note: Ai Ai differentiates between states where all players draw or win or lose; this is mostly to support cooperative games.
UCT Skill Chains
Match
AI
Strong Wins
Draws
Strong Losses
#Games
Strong Score
p1 Win%
Draw%
p2 Win%
Game Length
0
Random
2
UCT (its=3)
631
0
298
929
0.6485 <= 0.6792 <= 0.7085
45.21
0.00
54.79
81.20
8
UCT (its=9)
631
0
306
937
0.6427 <= 0.6734 <= 0.7027
43.54
0.00
56.46
81.19
22
UCT (its=23)
631
0
361
992
0.6057 <= 0.6361 <= 0.6655
47.28
0.00
52.72
81.45
46
UCT (its=47)
631
0
359
990
0.6069 <= 0.6374 <= 0.6667
49.70
0.00
50.30
81.22
49
UCT (its=50)
492
0
508
1000
0.4611 <= 0.4920 <= 0.5230
48.20
0.00
51.80
81.14
50
UCT (its=50)
487
0
513
1000
0.4561 <= 0.4870 <= 0.5180
49.50
0.00
50.50
81.08
Search for levels ended: time limit reached.
Level of Play: Strong beats Weak 60% of the time (lower bound with 95% confidence).
Draw%, p1 win% and game length may give some indication of trends as AI strength increases.
1st Player Win Ratios by Playing Strength
This chart shows the win(green)/draw(black)/loss(red) percentages, as UCT play strength increases. Note that for most games, the top playing strength show here will be distinctly below human standard.
Complexity
Game length
NaN
Branching factor
NaN
Complexity
10^NaN
Based on game length and branching factor
Computational Complexity
10^-∞
Saturation reached - accuracy very high.
Samples
1000
Quantity of logged games played
Computational complexity (where present) is an estimate of the game tree reachable through actual play. For each game in turn, Ai Ai marks the positions reached in a hashtable, then counts the number of new moves added to the table. Once all moves are applied, it treats this sequence as a geometric progression and calculates the sum as n-> infinity.
Move Classification
Board Size
52
Quantity of distinct board cells
Distinct actions
187
Quantity of distinct moves (e.g. "e4") regardless of position in game tree
Good moves
0
A good move is selected by the AI more than the average
Bad moves
187
A bad move is selected by the AI less than the average
Terrible moves
2
A terrible move is never selected by the AI Terrible moves: Edge 102:d6/e5,Edge 182:h8/g8
Samples
1000
Quantity of logged games played
Board Coverage
A mean of NaN% of board locations were used per game.
Colour and size show the frequency of visits.
Game Length
Game length frequencies.
Mean
79.75
Mode
[81]
Median
69.0
Change in Material Per Turn
Mean change in material/round
2.67
Complete round of play (all players)
This chart is based on a single representative* playout, and gives a feel for the change in material over the course of a game. (* Representative in the sense that it is close to the mean length.)
Actions/turn
Table: branching factor per turn, based on a single representative* game. (* Representative in the sense that it is close to the mean game length.)
Action Types per Turn
This chart is based on a single representative* game, and gives a feel for the types of moves available throughout that game. (* Representative in the sense that it is close to the mean game length.)
This chart shows the best move value with respect to the active player; the orange line represents the value of doing nothing (null move).
The lead changed on 7% of the game turns. Ai Ai found 1 critical turn (turns with only one good option).
Position Heatmap
This chart shows the relative temperature of all moves each turn. Colour range: black (worst), red, orange(even), yellow, white(best).
Good/Effective moves
Measure
All players
Player 1
Player 2
Mean % of effective moves
64.03
62.16
65.93
Mean no. of effective moves
22.52
21.56
23.50
Effective game space
10^79.01
10^38.57
10^40.43
Mean % of good moves
50.73
88.82
11.69
Mean no. of good moves
27.86
47.39
7.85
Good move game space
10^60.10
10^48.39
10^11.71
These figures were calculated over a single game.
An effective move is one with score 0.1 of the best move (including the best move). -1 (loss) <= score <= 1 (win)
A good move has a score > 0. Note that when there are no good moves, an multiplier of 1 is used for the game space calculation.
Quality Measures
Measure
Value
Description
Hot turns
64.20%
A hot turn is one where making a move is better than doing nothing.
Momentum
24.69%
% of turns where a player improved their score.
Correction
37.04%
% of turns where the score headed back towards equality.
Depth
4.65%
Difference in evaluation between a short and long search.
Drama
0.04%
How much the winner was behind before their final victory.
Foulup Factor
75.31%
Moves that looked better than the best move after a short search.
Surprising turns
1.23%
Turns that looked bad after a short search, but good after a long one.
Last lead change
62.96%
Distance through game when the lead changed for the last time.
Decisiveness
7.41%
Distance from the result being known to the end of the game.
These figures were calculated over a single representative* game, and based on the measures of quality described in "Automatic Generation and Evaluation of Recombination Games" (Cameron Browne, 2007). (* Representative, in the sense that it is close to the mean game length.)
Openings
Moves
Animation
Edge 1:d2/d1,Edge 2:e1/d1,e8
Edge 2:e1/d1,Edge 1:d2/d1,e8
Edge 161:a8/-,Edge 162:b8/a8,e8
Edge 162:b8/a8,Edge 161:a8/-,e8
Opening Heatmap
Colour shows the success ratio of this play over the first 10moves; black < red < yellow < white.
Size shows the frequency this move is played.
Unique Positions Reachable at Depth
0
1
2
3
4
5
1
187
527
18207
3208615
8928199
Note: most games do not take board rotation and reflection into consideration. Multi-part turns could be treated as the same or different depth depending on the implementation. Counts to depth N include all moves reachable at lower depths. Inaccuracies may also exist due to hash collisions, but Ai Ai uses 64-bit hashes so these will be a very small fraction of a percentage point.
Shortest Game(s)
No solutions found to depth 5.
Puzzles
Puzzle
Solution
Red to win in 10 moves
Red to win in 7 moves
Purple to win in 7 moves
Purple to win in 7 moves
Red to win in 4 moves
Weak puzzle selection criteria are in place; the first move may not be unique.
